class_docstr: A variety of graph kernel benchmark datasets, *.e.g.*,
  :obj:`"IMDB-BINARY"`, :obj:`"REDDIT-BINARY"` or :obj:`"PROTEINS"`,
  collected from the `TU Dortmund University
  <https://chrsmrrs.github.io/datasets>`_.
  In addition, this dataset wrapper provides `cleaned dataset versions
  <https://github.com/nd7141/graph_datasets>`_ as motivated by the
  `"Understanding Isomorphism Bias in Graph Data Sets"
  <https://arxiv.org/abs/1910.12091>`_ paper, containing only non-isomorphic
  graphs.
  
  .. note::
      Some datasets may not come with any node labels.
      You can then either make use of the argument :obj:`use_node_attr`
      to load additional continuous node attributes (if present) or provide
      synthetic node features using transforms such as
      :class:`torch_geometric.transforms.Constant` or
      :class:`torch_geometric.transforms.OneHotDegree`.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The `name
          <https://chrsmrrs.github.io/datasets/docs/datasets/>`_ of the
          dataset.
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
      use_node_attr (bool, optional): If :obj:`True`, the dataset will
          contain additional continuous node attributes (if present).
          (default: :obj:`False`)
      use_edge_attr (bool, optional): If :obj:`True`, the dataset will
          contain additional continuous edge attributes (if present).
          (default: :obj:`False`)
      cleaned (bool, optional): If :obj:`True`, the dataset will
          contain only non-isomorphic graphs. (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 20 10 10 10 10 10
      :header-rows: 1
  
      * - Name
        - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - MUTAG
        - 188
        - ~17.9
        - ~39.6
        - 7
        - 2
      * - ENZYMES
        - 600
        - ~32.6
        - ~124.3
        - 3
        - 6
      * - PROTEINS
        - 1,113
        - ~39.1
        - ~145.6
        - 3
        - 2
      * - COLLAB
        - 5,000
        - ~74.5
        - ~4914.4
        - 0
        - 3
      * - IMDB-BINARY
        - 1,000
        - ~19.8
        - ~193.1
        - 0
        - 2
      * - REDDIT-BINARY
        - 2,000
        - ~429.6
        - ~995.5
        - 0
        - 2
      * - ...
        -
        -
        -
        -
        -
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code: def num_edge_attributes(self) -> int:
      return self.sizes["num_edge_attributes"]
  
file_path: pytorch_geometric/torch_geometric/datasets/tu_dataset.py
imports: import os.path
  from typing import Callable, List, Optional
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.io import fs, read_tu_data
label_desc: Use image node_id 7 for calling the TUDataset obj's underlying member method code with example usage: obj.num_edge_attributes() and returns: self